The telecommunications industry has worked for over a decade to develop digital subscriber line (DSL) technologies to revitalize the embedded copper plant. Now the industry is on the threshold of reaping the benefits of this effort, bringing megabit-per-second connectivity to offices and homes. However, network providers must properly manage the introduction and application of these systems to the local network. Accurate engineering is critical to avoid provisioning failures that would otherwise create customer disappointment, delay and higher costs. An understanding of subscriber loop make-ups, including length, wire gauge and location of bridged taps, is key to the proper engineering of DSL systems. While some loop records exist, they may be inaccurate or out of date. Knowing the types of systems that are transmitting in a given cable is also critical, because of the resultant crosstalk and the potential need for spectrum management.
High-speed DSL access for small business and residential customers is one of the most important new services currently being offered by network and service providers. To date, DSL service providers are still in need of an advanced loop qualification system that can minimize qualification errors. Loop qualification consists of determining whether a loop can support DSL services or not and, generally, the estimate of the transfer function of the loop is sufficient for such purposes. Successful loop qualification is perhaps the most critical step in signing up new customers. Loop make-up identification will allow operators to pre-qualify a loop for DSL service, and also to update and reorder telephone company loop-records, which can be accessed to support engineering, provisioning and maintenance operations.
Among the new techniques being used to upgrade the public network are access technologies that use the embedded copper plant to support higher bandwidth services. These technologies include ISDN, HDSL, ADSL, SHDSL, ADSL2+, VDSL and others for which carriers have announced aggressive deployment plans. The new DSL technologies have the potential to become strategically important to an operator's business plans. While they have proven robust in trials, any new technology, no matter how powerful and reliable, must be properly deployed. This involves several engineering tasks including loop qualification and spectrum management. Loop qualification consists of knowing what type of DSL system and what bit-rate can be successfully and reliably provisioned on a given customer's loop. This is based on loop composition data.
Maintaining accurate records of the loop plant is important to many aspects of an operator's business. Beyond supporting traditional voice services, even more accurate and detailed loop records are needed when deploying DSL-based services. POTS services generally only require that loops meet Revised Resistance Design (RRD) rules (up to about 18 kilofeet). DSL technologies are capable of transmitting variable rates, and admit different service levels on different loops.
Several approaches and tools are being developed to facilitate loop qualification. The most common is mining existing data in loop databases, checking it for accuracy, and then bulk-provisioning loops that are candidates for DSL-based service. Sometimes a combination of loop records and engineering information about feeder route topology is used to obtain an estimate of loop length. Another technique uses loop loss measurements from traditional POTS loop testing systems to estimate loop length. These approaches use good engineering judgment and result in large populations of loops that are likely candidates to support advanced services. However, a typical approach is to minimize customer disappointment by biasing the techniques toward low probabilities of false-positive results. This is done at the expense of increasing the probability of false-negative results. In other words, the approaches are conservative, and some loops that might support DSL service remain unused or run at low bit-rates. Other loop qualification methods use the adaptation settings of a modem operating over the subscriber loop of interest. For example, if a DSL system is operational, the adaptive filter settings contain useful information about the loop. This approach has the disadvantage that a DSL system must first be in operation on the loop which one hopes to qualify. Other techniques use standard dial-up modems that operate over any subscriber loop to glean voice-band information about the loop, in order to predict performance at the higher frequencies of interest to DSL.
It would be desirable to have a single-ended testing technique that could estimate the transfer function or identify (and, therefore, qualify) all nonloaded subscriber loops without the need for special equipment or intervention at the subscriber's location. Loop make-up identification implies loop qualification and also allows telephone companies to update and correct their loop plant records. Therefore, accurate loop make-up identification can further be used to update records in loop databases, where records can in turn be accessed to support engineering, provisioning and maintenance operations. The importance of this capability is also confirmed by the creation of a new project in ITU-T SG15 Q4 on single-ended loop test (G.selt). G.selt modems will report single-ended measurements from a single DSL modem, before DSL service is activated or to analyze DSL lines that aren't working. G.selt modems may measure frequency dependent impedance, TDR signals, noise spectrum at the CO, impulse noise counts, etc. This may be done to determine: loop length, loop make-up, crosstalker types, crosstalk couplings, radio ingress, impulse noise, linearity, SNR and bit-rate capacities, load coils, etc. G.selt modems are likely to provide data to a separate analysis engine, which interfaces with a DSL Operating Support System.
Despite its importance, there is little published research on algorithms or techniques that allow loop make-up identification or even channel transfer estimation via single-ended testing. In “Estimation of the Transfer Function of a Subscriber Loop by Means of a One-Port Scattering Parameter Measurement at the Central Office” by T. Bostoen, et al., IEEE J. Select Areas Commun., pp. 936–948, it is proposed to use the one-port scattering parameter to achieve channel transfer function estimation when a priori information on the loop topology is available. Although this technique provides good results on short/medium length loops, the assumption that some or all the loop topology is known prior to testing may limit the practical applicability of this technique.
In Time Domain Reflectometry (TDR) approaches, i.e. probing signals are transmitted onto the loop and echoes reflected by impedance changes are analyzed to infer the unknown loop topology. Previous attempts to use TDR techniques, sometimes coupled to artificial neural network algorithms, have failed due to the difficulty of the post-processing of the TDR trace needed to extract all loop. Moreover, conventional metallic TDRs are not capable of detecting all echoes. In fact, conventional metallic TDRs cannot detect gauge changes and, moreover, have a serious range limitation that prevents them from detecting reliably echoes farther than several kilofeet (kft) from the Central Office (CO). This limitation is essentially due to two reasons: conventional TDR methods use unbalanced probing thus allowing for limited common mode rejection capability; there is a slowly decaying signal (SDS) caused by the distributed RLC nature of the loop, that overlaps with and masks the echoes generated by impedance changes.
Accurate TDR measurements alone are not sufficient without an algorithm able to extract information from the TDR trace. In particular, a major problem arises in a TDR approach since observations available at the receiver consist of an unknown number of echoes, some overlapping, some not, some spurious, some not, that exhibit unknown amplitude, unknown time of arrival and unknown shape. The resolution of such echoes via a single sensor (and not an array) is very complicated and has seldom been addressed. Real echoes are defined herein as he echoes pertaining to initial encounters with discontinuities, whereas we define as spurious echoes all the echoes caused by successive reflections. The necessity of separating echoes in two categories (“real” and “spurious”) is irrelevant for modeling issues, but it becomes important when loop identification is attempted. In fact, any identification algorithm must be able to discriminate between real echoes (the echoes that indicate the actual presence of a real discontinuity) and spurious echoes (the re-reflected and artificial echoes that do not indicate the presence of a discontinuity
In commonly-assigned U.S. Pat. No. 6,538,451 entitled “Single Ended Measurement Method and System For Determining Subscriber Loop Make Up” to Galli et al., a method and system for determining the make-up of a subscriber loop by sending pulses onto a loop and acquiring data based on received echo signals caused by discontinuities as a pulse traverses a loop is disclosed. Note that Galli is also a co-inventor on the present invention. Although the Galli method is able to determine loop makeup more accurately and overcomes the prior art problems highlighted above, the method does have some shortcomings. First, the method works well only where the loop is less than approximately 8,000 feet (8 kft). Once the length of the loop increased significantly beyond 9 kft the method is not able to identify loop discontinuities with the same success because of noise enhancement due to the use of the reciprocal of the insertion loss. Second, Galli's method may not achieve unambiguous loop make-up identification if the topology of the loop under test does not belong to the set of “well-behaved” loops, i.e., loops that follow the recommended design rules.
In U.S. Pat. No. 6,724,859 entitled “Method For Determining Subscriber Loop Make-Up” to Galli (“Galli II”) this issue was overcome. In Galli II, the inductive effect of a subscriber loop was taken into account when processing echo signals that are the result of probing the loop with pulses by removing the slowly decaying signal caused by the inductive effect of the loop from the echo signals. Accordingly, the echo signals are no longer masked by the slowly decaying signal thereby increasing the accuracy and range of a measurement system built in accordance with the present invention. The input impedance of the loop as a function of frequency was used in the process of identifying discontinuities and other features represented by the echo signals. This is accomplished by first calculating the input impedance of the loop as a function of frequency. The input impedance of the loop is then convolved, in the frequency domain, with the Fourier transform of the probing signal. Finally, a simulated waveform of the discontinuity in the time domain is obtained by inverse Fourier transforming the result of the convolution. This simulated waveform is then compared to the actual echo signal caused by the discontinuity. If the comparison yields an acceptable match, e.g., within a predetermined error margin, then the discontinuity is identified and the signal corresponding to that discontinuity is removed by subtracting the simulated waveform from the acquired data. This is done for each discontinuity encountered until the last discontinuity is identified using the maximum likelihood (ML) approach.
The prior approach does not take into account information that is known about the loop plant using a maximum a-posteriori probability (MAP) estimation. Therefore, it would be desirable to have a system and method that uses such a MAP estimation to use known loop plant information to increase the accuracy of the loop composition identification.
The prior approach successively estimated loop sections from a single previous estimate of the loop make-up. There was no way of recovering from errors made early in the process. Therefore, it would be desirable to have a system and method that builds on multiple estimates to allow recovery from errors and provide improved estimation.